Airborne LiDAR has become an essential data source for large-scale,
high-resolution modeling of forest aboveground biomass and carbon
stocks, enabling predictions with much higher resolution and accuracy
than can be achieved using optical imagery alone. Ground noise filtering
– that is, excluding returns from LiDAR point clouds based on simple
height thresholds – is a common practice meant to improve the `signal’
content of LiDAR returns by preventing ground returns from masking
useful information about tree size and condition contained within canopy
returns. However, ground returns may be helpful for making accurate
aboveground biomass predictions in heterogeneous landscapes that include
a patchy mosaic of vegetation heights and land cover types. In this
paper, we applied several ground noise filtering thresholds while
mapping forest AGB across New York State (USA), a heterogenous landscape
composed of both contiguously forested and highly fragmented areas with
mixed land cover types. We fit random forest models to predictor sets
derived from each filtering intensity threshold and compared model
accuracies, paying attention to how changes in accuracy correlated with
landscape structure. We observed that removing ground noise via any
height threshold systematically biases many of the LiDAR-derived
variables used in AGB modeling, with mean correlation (Spearman’s ρ)
between variables increasing from 0.183 to 0.266. We found that that
ground noise filtering yields models of forest AGB with lower accuracy
than models trained using predictors derived from unfiltered point
clouds, with RMSE increasing by up to 2.2 Mg ha-1 statewide. Although we
only modeled AGB for forest cover types, models fit to predictors
derived from filtered point clouds performed worse as landscape
heterogeneity (as measured by patch density and edge density) increased,
suggesting ground returns are particularly useful when modeling edge
forests. Our results suggest that ground filtering should be a carefully
considered decision when mapping forest AGB, particularly when mapping
heterogeneous and highly fragmented landscapes, as ground returns are
more likely to represent useful `signal’ than extraneous `noise’ in
these cases.

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